_base_ = [
    "../_base_/models/ssd300.py",
    "../_base_/datasets/voc0712.py",
    "../_base_/default_runtime.py",
]
model = dict(
    bbox_head=dict(
        num_classes=20, anchor_generator=dict(basesize_ratio_range=(0.2, 0.9))
    )
)
# dataset settings
dataset_type = "VOCDataset"
data_root = "data/VOCdevkit/"
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
train_pipeline = [
    dict(type="LoadImageFromFile", to_float32=True),
    dict(type="LoadAnnotations", with_bbox=True),
    dict(
        type="PhotoMetricDistortion",
        brightness_delta=32,
        contrast_range=(0.5, 1.5),
        saturation_range=(0.5, 1.5),
        hue_delta=18,
    ),
    dict(
        type="Expand",
        mean=img_norm_cfg["mean"],
        to_rgb=img_norm_cfg["to_rgb"],
        ratio_range=(1, 4),
    ),
    dict(
        type="MinIoURandomCrop", min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), min_crop_size=0.3
    ),
    dict(type="Resize", img_scale=(300, 300), keep_ratio=False),
    dict(type="Normalize", **img_norm_cfg),
    dict(type="RandomFlip", flip_ratio=0.5),
    dict(type="DefaultFormatBundle"),
    dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
]
test_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(
        type="MultiScaleFlipAug",
        img_scale=(300, 300),
        flip=False,
        transforms=[
            dict(type="Resize", keep_ratio=False),
            dict(type="Normalize", **img_norm_cfg),
            dict(type="ImageToTensor", keys=["img"]),
            dict(type="Collect", keys=["img"]),
        ],
    ),
]
data = dict(
    samples_per_gpu=8,
    workers_per_gpu=3,
    train=dict(type="RepeatDataset", times=10, dataset=dict(pipeline=train_pipeline)),
    val=dict(pipeline=test_pipeline),
    test=dict(pipeline=test_pipeline),
)
# optimizer
optimizer = dict(type="SGD", lr=1e-3, momentum=0.9, weight_decay=5e-4)
optimizer_config = dict()
# learning policy
lr_config = dict(
    policy="step", warmup="linear", warmup_iters=500, warmup_ratio=0.001, step=[16, 20]
)
checkpoint_config = dict(interval=1)
# runtime settings
runner = dict(type="EpochBasedRunner", max_epochs=24)
